Active Learning of Binary Classifiers

In supervised machine learning, well-designed interactive learning
algorithms can provide valuable improvements over passive algorithms
in learning performance while reducing the amount of effort required
of a human annotator. This has been observed in practice, but only
recently have we begun to understand the conditions under which active
learning can and cannot yield significant improvements. In this talk,
we will survey some recent progress toward provably good active
learning algorithms for learning linear separators. We will also
briefly cover some recent progress toward understanding the sample
complexity of active learning in general.